Interactions of coronary artery smooth muscle cells with 3D porous polyurethane scaffolds
Bibliographic record
Abstract
One strategy in vascular tissue engineering is the design of hybrid vascular substitutes where vascular cells infiltrate biostable porous scaffolds that provides favorable environment for guided cell repopulation and acts as a mechanically supporting layer after the tissue regeneration process. The aim of the present work was to study the interaction of human coronary artery smooth muscle cells (HCASMC) with 3D porous polyurethane scaffolds. We therefore fabricated porous and highly interconnected 3D polyurethane scaffolds that can promote HCASMC attachment, proliferation, and migration. SEM and microCT studies of the fabricated scaffolds showed that the current scaffolds had highly open and interconnected pore structures, with an average porosity of 84%. HCASMC interaction on polyurethane films revealed that cells adhere and express specific marker proteins (vinculin and h-caldesmon). This expression was further enhanced by coating the polyurethane with Matrigel. On uncoated 3D scaffolds, dense spherical aggregates of cells were often encountered with little adhesion of individual cells alongside the struts of the scaffold, independent of the porogens used. In contrast, when cultured on Matrigel-coated scaffolds, cell numbers quickly increased after 14 days and spread along the entire scaffold. At the upper scaffold surface, elongated cells were seen adhering to one another and also to the scaffold surface. These cells were elongated, aligned in parallel and contained abundant F-actin bundles suggesting a differentiated contractile phenotype. Deep into the scaffold, cells were encountered that formed actin-rich lamellipodial extensions spreading along the strut and lacked stress fibers, suggesting active cell migration along the substrate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".